You're new to the data governance field. How can you get up to speed quickly?

There is no good/optimal (one-size-fits-all) strategy on how to govern a company's data or information. It'll depend on the sector we are in, the maturity of our company and its teams,.. which will help us decide whether we should go for Centralised or Federated governance, Defensive or Offensive, Enterperprise or at Company level. Obviously it is necessary to know what Data Governance means, since nowadays it doesn't make sense to govern only data but also all its USES, including above all Artificial Intelligence, so the first thing would be to always talk about DATA & AI GOVERNANCE as a single framework.

I would suggest anyone starting in data governance understand the strategic aspects that motivated the implementation, the people involved and their responsibilities, the strategic governance goals, and the results achieved so far. It is essential to talk to people and analyze cases of failure, the reasons for failures, and what was done to correct them. With this, it is possible to have a comprehensive overview of the company's current state of data governance.

What is needed for effective data governance will vary from organisation to organisation. While understanding the fundamentals is necessary, understanding the business you're in is arguably more important. What data is collected? How is it handled? What processes does it support? Relevant legislation? What are the data challenges that good governance can solve? Understanding the value of effective data governance within your organisation will help you prioritise your approach based on industry frameworks, legislative requirements, and business needs.

Data governance is not just a project or a thing to check off and be done with. It's a culture, a way of operating, and a commitment to data excellence across the enterprise. In my experience, taking the time to deeply understand the chosen framework, whether DAMA-DMBOK, DCAM, COBIT, or another industry-standard, is invaluable. It ensures consistency, delineates roles and responsibilities, and drives better decision-making around data assets. This knowledge empowers me to deliver higher-quality data products and services that create real value for the business. Embracing data governance as a culture, not just a checklist, is the key to unlocking data's true potential.

The harsh reality is that with topics such as Data Governance, Project Management, etc. you get a ton of theoretical, conceptual, text-book frameworks. However, it is extremely important to note that a cookie cutter approach can not be taken to successfully implement Data Governance within your organisation, because one size does not fit all. Work with your data champions, your organisational leaders to design data governance processes that suit the objectives, the culture and the data in YOUR organisation, because Data Governance does not need to become a burden, rather it needs to be something that empowers people by weaving it into their day jobs, their processes, their incentives - such that it becomes "the way we do business here".

Roles are part of the main pillars of data governance. There are two main roles in data governance: Data Owner and Data Steward. The data owner is accountable for a scope of data at the core of his/her business capabilities. He/she is the ultimate accountable for the quality and the availability of data within the company to serve the other business functions. Under the supervision of the data owner, data stewards are accountable for making the inventory of data related to their business capabilities. They are also in charge of defining and documenting data in the business glossary of the data catalog. They also have an operational role through data quality monitoring / correction and guidance for the right processing of data.

As part of the set of tools supporting data governance, there are two main types: tools covering semantics and rules, and tools covering processes and quality. In data governance, it's key to align concepts, meaning, and rules with data catalog and data modeling. The data catalog will enable to make inventory of data assets and have a common definition of data in a central place, while data modeling will enable to document the data structure. For processes and quality, the usual enablers of data governance are MDM, data quality tools, and business process modeling. These tools will enable definition and implementation of data management to ensure consistency and integrity of data across the organization.

Active participation in hands-on initiatives, in my experience, has proven to be particularly beneficial in reinforcing and increasing understanding of data governance. This hands-on experience is crucial because it allows you to test ideas, experiment with solutions and new technologies, and gain a deeper, more refined understanding of data governance in practice.

It is a good idea to seek mentorship from experienced data governance professionals and to join a community of data professionals such as DAMA. Such communities provide opportunities for knowledge sharing and mentoring. Getting first-hand stories of how experts started their data governance journey is another good way to get up to speed quickly in data governance.